1. Field of the Invention
The present invention generally concerns the construction, training and use of neural networks for the optimization of the administration of drugs (and, for the invention, drug equivalents such as food and exercise) in respect of patient characteristics.
The present invention particularly concerns the construction, training and use of neural networks to better recognize any of (1) optimal patient dosage of a single drug, (2) optimal patient dosage of one drug in respect of the patient""s concurrent usage of another drug, (3a) optimal patient drug dosage in respect of patient characteristics, (3b) sensitivity of patient recommended drug dosage to patient characteristics, (4a) expected outcome versus patient drug dosage, (4b) sensitivity of expected outcome to drug dosage, (5) expected outcome(s) from drug dosage(s) other than projected optimal dosage, from which expected outcome(s) costs both human and economic may be separately predicted.
2. Description of the Prior Art
Many ailments exist in society for which no absolute cure exists. These aliments include, to name a few, certain types of cancers, certain types of immune deficiency diseases and certain types of mental disorders. Although society has not found an absolute cure for these and many other types of disease, the use of drugs has reduced the negative effect of these disorders.
Generally the developers of drugs have two goals. First, they try to alter the drug user""s biochemistry to correct the physiological nature of the illness. Second, they try to reduce the drug""s negative side effects on the user. To accomplish these goals, drug developers utilize time consuming and scientifically advanced methods. These expensive efforts yield an extremely high cost for many drugs.
Unfortunately, when these costly drugs are distributed they are usually accompanied by only a crude system for assisting a doctor in determining an appropriate drug dosage for a patient. For instance, the annually printed Physician""s Desk Reference summarizes experimentally determined reasonable drug dosage ranges found in the research literature. These ranges are general. The same dosage range is given for all patients.
Other publications exist which provide general methods to assist a doctor in determining an appropriate dosage. These references and manuals are not, however, directed towards providing a precise dosage range to match a specific patient. Rather, they provide a broad range of dosages based on an averaging of characteristics over an entire population of patients. The correlations between distinguishing patient characteristics and actual required dosages are never obtained, even in the original research.
Faced with the task of minimizing side effects and maximizing drug performance, doctors sometimes refine the dosage they prescribe for a given individual by trial and error. This method suffers from a variety of deleterious consequences. During the period that it takes for trial and error to find an optimal drug dosage for a given patient, the patient may suffer from unnecessarily high levels of side effects or low or totally ineffective levels of relief. Furthermore, the process wastes drugs, because it either prescribes a greater amount of drug than is needed or prescribes such a small amount of drug that it does not produce the desired effect. The trial and error method also unduly increases the amount of time that the patient and doctor must consult.
The past few decades have produced research identifying numerous factors that influence the clinical effects of medication. Age, gender, ethnicity, weight, diagnosis and diet have all been found to influence both the pharmacokinetics and pharmacodynamics of drugs. As a result, it is now acknowledged that women, minorities, and the elderly often require considerably lower doses of some medications than their male Caucasian counterparts. Furthermore, it is possible that patient variables have potentially varying strengths of influence for each case, and each drug. For example, weight may be of greater importance than age for a Caucasian male while the converse may be true for an African American female. See Lawson, W. B. (1996). The art and science of psychopharmacotherapy of African Americans. Mount Sinai Journal of Medicine, 63, 301-305. See also Lin, K. M., Poland, R. E., Wan, Y., Smith, M. W., Strickland, T. L., and Mendoza, R. (1991). Pharmacokinetic and other related factors affecting psychotropic responses in Asians. Psychopharmacology Bulletin, 27, 427-439. See also Mendoza, R., Smith, M. W., Poland, R., Lin, K., Strickland, T. (!991). Ethnic psychopharmacology: The Hispanic and Native American perspective. Psychopharmacology Bulletin, 27, 449-461. See also Roberts, J., and Tumer, N. (1988). Pharmacodynamic basis for altered drug action in the elderly. Clinical Geriatric Medicine, 4, 127-149. See also Rosenblat, R., and Tang, S. W. (1987). Do Oriental psychiatric patients receive different dosages of psychotropic medication when compared with Occidentals? Canadian Journal of Psychiatry, 32, 270-274. See also Dawkins, K., and Potter, Z. (1991). Gender differences in pharmacokinetics and pharmacodynamics of psychotropics: Focus on women. Psychopharmacology Bulletin, 27, 417-426.
The large number of potentially interacting variables to consider, in addition to the wide therapeutic windows of many drugs (including psychotropic drugs) have resulted in prescribing practices that rely mainly upon trial-and-error and the experience of the prescribing clinician.
The compensation process can be quite lengthy while drug consumers experiment with varying dosages. New methods are needed to reduce the time to compensation for patients (including psychiatric patients), thus alleviating their suffering more quickly as well as reducing the cost of hospitalization. The optimization of drug dosages would also help avoid unnecessarily high dosages, reducing the severity of the many side effects that typically accompany such medications and increasing the likelihood of long-term compliance with the prescribed regimen.
For decades, researchers have recognized the need for finding new methods of accounting for inter-individual differences in drug response. See, for example, Smith, M., and Lin, K. M. (1996); A biological, environmental, and cultural basis for ethnic differences in treatment; In P. M. Kato, and T. Mann (Eds.), Handbook of Diversity Issues in Health Psychology (pp. 389-406); New York: Plenum Press; and also Lenert, L., Sheiner, L., and Blaschke, T. (1989). Improving drug dosing in hospitalized patients: automated modeling of pharmacokinetics for individualization of drug dosage regimens; Computational Methods in Programs Biomedical, 30, 169-176.
However, a practical solution to tailoring drug regimens has yet to be implemented on a widespread basis.
Pharmacological software currently in use attempts to provide guidelines for drug dosages, but most software programs merely access databases of information rather than compute drug dosages. At best, these databases rely upon existing research that groups subjects in a few gross categories (e.g., the elderly, or children), and they usually do not include information regarding such relevant characteristics as weight or ethnicity.
The few analytical software products that make use of computer algorithms base their recommendations primarily upon blood plasma concentrations of the drug of interest. See, for example, Tamayo, M., Fernandez de Gatta, M., Garcia, M., and Dominguez, G. (1992); Dosage optimization methods applied to imipramine and desipramine in enuresis treatment; Journal of clinical pharmacy and therapeutics, 17, 55-59; and also Lacarelle B., Pisano P., Gauthier T., Villard P. H., Guder F., Catalin J., and Durand A. (1994); Abbott PKS system: a new version for applied pharmacokinetics including Bayesian estimation; International Journal of Biomedical Computing, 36, 127-30.
Although these methods have met with some success in research, there are several major drawbacks to their implementation. The necessity for constant blood draws for each patient being monitored hinders their practicality in the clinical setting. Furthermore, the limitations of the algorithms used allow modeling of no more than a few select characteristics at a time, thus ignoring all others. Finally, the models inherently comprise a single algorithm.
However, various drugs have been demonstrated to exhibit quite different response curves. Most new methods use a Bayesian model, which allows for the incorporation of individual response characteristics. See, for example, Tamayo, et al., op. cit. and also Kaufmann G. R., Vozeh S., Wenk M., Haefeli, W. E. (1998). Safety and efficacy of a two-compartment Bayesian feedback program for therapeutic tobramycin monitoring in the daily clinical use and comparison with a non-Bayesian one-compartment model; Therapeutic Drug Monitoring, 20, 172-80. Even so, the user must first select one rigid modeling equation.
Recent research has begun to demonstrate that the flexibility of neural networks in trying a variety of algorithms reduces the margin of error in prediction of blood plasma levels. See Brier, M. E., and Aronoff, G. R. (1996); Application of neural networks to clinical pharmacology; International Journal of Clinical Pharmacology and Therapeutics, 34, 510-514.
The past two to three years have produced a proliferation of studies in the application of neural nets to clinical pharmacology. For example, neural networks are now being used to automate the regulation of anesthesia. See Huang, J. W., Lu, Y. Y., Nayak, A., Roy, R. J. (1999); Depth of anesthesia estimation and control; IEEE Trans Biomedical Engineering, 46, 71-81.
Neural networks are used to determine optimal insulin regimens. See Trajanoski, Z., and Wach, P. (1998); Neural predictive controller for insulin delivery using the subcutaneous route; IEEE Trans Biomedical Engineering, 45, 1122-1134; and also Ambrosiadou, B. V., Gogon, G., Maglaveras, N., Pappas, C. (1996); Decision support for insulin regime prescription based on a neural net approach; Medical Information, 21, 23-34.
Neural networks are even used to predict clinical response to other medications. See Brier, M. E., et. al., op. cit. and also Bourquin, J., Schmidli, H., van Hoogevest, P., Leuenberger, H. (1997); Application of artificial neural networks (ANN) in the development of solid dosage forms; Pharmacology Development Technology, 2, 111-21.
However, few, if any, prior art references consider the influence of ethnicity. And none known to the inventors envision the comprehensive neural network optimization that will seen to be the subject of the present invention.
The full potential of neural network applications in medicine has yet to be realized, but their growing popularity has resulted in more sophisticated methodology. For example, a genetic algorithm was used to reduce the number of variables required for the training of a neural net in the prediction of patient response to the drug Warfarin. See Narayanan, M. N., and Lucas, S. B. (1993); A genetic algorithm to improve a neural network to predict a patient""s response to Warfarin; Methods in Information Medicine, 32, 55-58.
However, most current models used in research are dated and not as efficient as those yet to be publicized such as the preferred Levenberg-Marquardt technique used in the present invention, and explained in detail below. Furthermore, although genetic algorithms have recently been used in the neurocomputing field to optimize network architectures, these research techniques have yet to be translated to the medical community or to medical applications (as is the subject of the present invention).
It will be seen that both diet and exercise can be considered equivalent to drugs for purposes of applying the present invention. Equivalently to the often existing uncertainties with which patient characteristics of age, sex, ethnicity, etc., correlate with optimal drug dosage, it is often uncertain as to how exercise and/or diet will affect individuals of certain characteristics as regards induced changes in weight and/or blood pressure. Equivalently to the often existing uncertainties regarding the effects to be expected from changing the dosage of a drug, it is often difficult to answer for an individual patient questions such as xe2x80x9cHow little to I have to eat for how long to lose 50 pounds?xe2x80x9d or xe2x80x9cHow much weight must I lose to lower my blood pressure into a safe range?xe2x80x9d The present invention will be seen to be useful in reducing uncertainties, and in answering questions, in the areas of diet and exercise management as well as drug dosage estimation.
The present invention contemplates making (i.e., programming), training (optimizing) and using neural networks (i) in order to estimate the optimal dosage of one or more drugs for a particular patient, as well asxe2x80x94likely equally or more importantlyxe2x80x94(ii) to render better visible many factors concerning the proper dosage(s) of drugs (and drug equivalents, such as diet and exercise), and the sensitivity of both drug dosage(s) and therapeutic outcomes to these factors.
The neural-network-based, computerized, drug dosage estimator of the present invention combines a number of variables influencing drug response into a single empirical computer model that can be easily used to (i) refine prescribing practices (including on the individual patient level), as well as to (iii) generate future hypothesis testing regarding the underlying mechanisms of each component.
In simplest terms, the neural network drug dosage estimator of the present invention predicts optimal drug dosages for populations or individuals based on the multi-faceted characteristics of such populations or individuals. Because the drug dosage estimation model can be run for populations of various characteristics, it is clearly possible to quickly learn which population or patient characteristic(s) is (are) of greatest significance for each drug. For example, ethnicity is believed by the inventors to presently (circa 1999) be an underweighted factor in the prescription of many drugs, and especially psychotropic drugs.
Less clearly, it is possible to exercise the computerized neural network drug dosage estimator of the present invention to predict what will happen when a patient, or a patient population, is administered a drug dosage deviating from predicted optimal. For example, if an individual patient of certain characteristics does not exhibit desired therapeutic response at a certain (possibly even the predicted optimal) drug dosage level, then should the dosage be increased by 10%, or by 25%, or by 50%, or even by 100%? Exercise of the neural network drug dosage estimator of the present invention helps to definitively answer this question.
A drug dosage neural network of the present invention arguably presents a major innovation in pharmacology because it works, and works well. The drug dosage neural network does so work particularly because its architecture has been optimized (via competitive selection) relative to real-world historical clinical data. Still more particularly, the drug dosage neural network works and works well because its optimal neural net architecture is selected using an advanced techniquexe2x80x94a genetic algorithm. Such neural networks as have heretofore been employed in the health sciences have not been optimized at all to the best knowledge of the inventors, and certainly not by use of a fast genetic algorithm.
The primary objective of the present invention is the realization of an algorithmically-based, computerized, accurate optimization of drug dosage, including psychotropic drug dosage, for an individual patient based on data regarding that individual patient. The computerized drug dosage estimator of the present invention is based on a neural network coupled with a genetic algorithm to map clinically determined stabilizing dosages of several drugs as a function of individual characteristics.
A neural net architecture especially suited to this problem, including a specific genetic algorithm to increase modeling accuracy, is taught within this specification. Greater accuracy and finer precision of drug dosage ranges, including psychotropic drug dosage ranges, are realizable by the simple-to-use, non-intrusive tool of the present invention employing a computer algorithm that accounts for the many variables that influence clinical drug response.
The practical effect of the present invention is to alleviate much of the guesswork in prescribing medication, particularly including psychotropic medication, and to thus reduce the number and severity of side effects, and/or sub-optimal or ineffective therapeutic effectiveness, unnecessarily suffered by an individual patient when the drug dosage prescribed for such patient is incorrect. The inventors project that the optimization of drug dosages accorded by the present invention will most greatly succor those populations that have historically been the most sensitive to medications: women, children, minority groups, and the elderly. However, the optimization of drug dosages accorded by the present invention will benefit us all by alleviating the tremendous wastage or drugs, and prolongation of illness, that results from the proscribing of drugs at a non-optimal levels, individual patient by individual patient.
The computerized neural networks of the present invention are derived from, and are proven upon, actual historical patient data concerning the administration of, and results from, drug therapies. The neural networks are derived: they are not strictly dependent upon what their originatorxe2x80x94a neural network architectxe2x80x94initially thinks to be the proper choice(s) of, and interplay between, patient factors in accordance with which drug dosages would presumptively best be prescribed. (xe2x80x9cPatient factorsxe2x80x9d include things like (1) overt indications of (1a) age, (1b) gender, (1c) race, (1d) ethnicity, (1e) diet type, (1f) height, (1g) weight, and (1h) body surface area; (2) medical diagnostic indications of (2a) blood pressure, (2b) use of a drug other than the particular drug at the same time as use of the particular drug, (2c) fitness, (2d) peptide levels, and (2e) genetic predisposition to a particular disease; and (3) pharmacological indications of (3a) pharmacokinetic parameters and (3b) pharmacodynamic parameters.)
Instead, the neural networks are selected, or optimized, by and in a standard genetic selection algorithm. What is derived, after 5, or 10 or even 20 iterations is a single selected optimal neural network.
In accordance that the optimal neural network is selected by empirical historical patient data drug response data, this network mayxe2x80x94and often does at the present state of advance in the medical arts for the precision administration of many drugs especially including psychotropic drugsxe2x80x94deliver optimal drug dosage results that may come as a great surprise to a physician prescriber of the drug, downplaying or ignoring factors that physicians have deemed important and elevating factors that were previously ignored, or perceived to be of lesser importance. For example, patient race and ethnicity turn out to be an unexpectedly important factors in the administration of psychotropic drugs. (It will be understood, however, that the present invention is not a drug dosage scheme for any particular drug, or class of drugs, but a methodology for determining optimal drug dosage.)
Simultaneously to being selected, the optimal neural network becomes trained on the historical patient data. (It may thus be said the optimal trained neural network is the one being selected.) This training may be, by way of example, in accordance with either the (i) Levenberg-Marquardt (L-M) or the (ii) back propagation methods of neural network optimization.
This (i) optimization (selection) and (ii) training of neural networks produces an optimized trained neural networkxe2x80x94a programmed computer processxe2x80x94that is very effective to predict from patient characteristics an optimal therapeutic drug dosage.
For example, the inventors have found that optimized trained neural networks for predicting the optimal dosage(s) of psychotropic drugs show a large and unexpected dependence upon the patient""s race and ethnicity. Therefore, and although patient factors like sex, age and weight might well be expected to be taken into consideration by a skilled prescribing physician, the neural network may, by its very existence, serve to highlight additional patient factors, such as race, that are suitably considered in prescribing drugs at an optimal level.
Under the laws of all States, a machine, even a computer-based neural network program, cannot prescribe drugs. The method, and the diverse optimized trained neural networks, of the present invention are used to support physician prescription of drugs. They do so by identifying and illuminating factors (mostly patient factors, but also cost factors) of relevance to drug therapy, and to optimized drug therapy. Accurate identification of these factors could potentially save millions of dollars in drugs prescribed and taken at levels to low to be effective, or at levels higher than are useful.
The present invention can also illuminate (ii) the sensitivity of therapeutic outcomes to these factors. One physician may modify his/her predilection for minimal dosages when he/she observes that exercise of an optimized neural network program for a Particular Patient not only indicates a higher recommended dosage, but predicts total pharmacological ineffectiveness at the level the physician desires to prescribe. Another physician may modify his/her predilection for potent dosages when he/she observes that exercise of an optimized neural network program for a Particular Patient not only indicates a lower recommended dosage, but predicts an avalanche of side effects at the level the physician desires to prescribe.
Even a neural network relating the dosage between two or more drugs taken concurrently by a single patient may be developed, optimized and trained, serving thereafter to predict the proper dosage of each drug in respect of the patient""s concurrent consumption of the other.
For example, a neural network need not be exercised solely to predict optimal drug dosage when all other factors are known, and are supplied as inputs to the solution of the neural network. The network may instead be exercised to assess the sensitivityxe2x80x94in terms of expected therapeutic outcomes of a drug therapy for a particular patientxe2x80x94when drug dosage is varied. Consider, for example, a patient that is not responding as desired at drug dosage xe2x80x9cXxe2x80x9d. This dosage xe2x80x9cXxe2x80x99 may have even been the dosage predicted optimal by the neural network; it matters not. The important thing is, the present dosage proving inadequate, should the dosage be increased to 1.5X? to 2X? to 3X? Suppose the neural network, when exercised with hypothetical drug dosages, shows a very sharp xe2x80x9conset of effectivenessxe2x80x9d coupled with adverse side effects at xe2x80x9coverdosesxe2x80x9d. This might mean that the attending physician might try and xe2x80x9cease intoxe2x80x9d the correct drug dosage level, initially increasing the drug dosage to, by way of example, only 1.5X.
For example, the sensitivity of drug dosage to a factor like patient weight can be examined. Suppose a patient performing satisfactorily on a drug at a conventional, predicted, optimal dosage level both (i) loses weight and (ii) commences to complain about drug side effects. Can the drug dosage safely be lowered while retaining therapeutic effectiveness? Some exercise of an appropriate neural network can provide insight into answering this question.
For example, the effect of one drug upon the recommended optimal dosage of another drug, and vice versa, may be examined with a drug interaction neural network in accordance with the present invention.
The neural networks of the present invention may be used as a sophisticated filter to isolate and examine the propensities and proclivities of drug-prescribing physicians. Does a physician frequently prescribe drugs at higher, or lower, dosages than a target optimal range? Does he or she routinely ignore sex and/or weight, giving the same drug dosages to small women as to large men? Or are the patterns of the drug-dispensing physician more subtle, such as a general practitioner who, while having never in his/her entire career made a recorded referral for high blood pressure in a patient less than fifty years of age, also has a personal map of drugs historically prescribed that is all but totally devoid of any anti-hypertensives whatsoever, showing only two prescriptions for reserpine (an antiquated medicine) in five years? It is of course possible that this physician""s entire patient population has contained abnormally few hypertensives. It is also possible that this physician is not rendering xe2x80x9cstate to the artxe2x80x9d patient service in this medical area.
The optimized and trained neural networks of the present invention are motivated by, and used for, improved patient care. However, improved patient care is not inconsistent with reduced costs, and cost control. In the first place, rendering any patient an optimally effective drug dosage for that patient may shorten the course of treatment, reduce patient non-compliance with prescribed drug therapies, reduce or eliminate undesirable side effects, and expedite cure. Consideration of the aggregate statistics of a great number of patients undergoing drug therapy at the level of, for example, a health maintenance organization or a governmental program such as Medicare, may permit the recognition of improved effective regimens of treatment, reducing cost attendant upon wastage. It is also at this high level where deviant drug prescription and/or consumption patterns and trends may be noticed, quantified and examined.
Therefore, in one of its aspects the present invention can be considered to be embodied in a computerized method of predicting an optimal dosage of a particular drug for a Particular Patient in consideration of previously determined optimal dosages of the drug for members of a patient population.
The computerized optimal drug dosage prediction method commences with the programming a neural network having an architecture of one or more slabs, the slabs collectively relating xe2x80x9cinput dataxe2x80x9d to xe2x80x9coutput dataxe2x80x9d. The xe2x80x9cinput dataxe2x80x9d includes at least a selected three (3) of a person""s traits drawn from at least two (2) of the three (3) groups consisting of Group 1 overt indications of (1a) age, (1b) gender, (1c) race, (1d) ethnicity, (1e) diet type, (1f) height, (1g) weight, and (1h) body surface area; Group 2 medical diagnostic indications of (2a) blood pressure, (2b) use of a drug other than the particular drug at the same time as use of the particular drug, (2c) fitness, (2d) peptide levels, and (2e) genetic predisposition to a particular disease; and Group 3 pharmacological indications of (3a) pharmacokinetic parameters, (3b) pharmacodynamic parameters. (It might be wondered just what is so important about xe2x80x9cat least a selected three (3) of a person""s traits drawn from at least two (2) of . . . [some] three (3) groupsxe2x80x9d. The answer is: the number and diversity of traits are simply to help to quantitatively distinguish the present invention over previous dosage charts that may plot, by way of example, a family of curves relating recommended dosage by age, weight and sex (i.e., by three traits).)
The xe2x80x9coutput dataxe2x80x9d is the clinically-determined optimal drug dosage for the same person.
Each of the programmed neural networks is trained with a training data set drawn from a large number of historical medical records of a large number of persons historically administered the particular drug, the records relating the selected input data to the output data.
One of the neural networks that performs best on the training data set is selected, becoming a xe2x80x9cselected trained neural networkxe2x80x9d.
This selected trained neural network is thereafter used to predict an optimal dosage of the particular drug for the Particular Patient. This using transpires by inputting the selected input dataxe2x80x94which input data is at least a selected three (3) of The Particular Patient""s traits drawn from at least two (2) of the three (3) groups consisting of Group 1 overt indications and Group 2 medical diagnostic indications and Group 3 pharmacological indicationsxe2x80x94in order to ascertain as (2) output of the trained neural network the output dataxe2x80x94which output data is the predicted optimal dosage for the Particular Patient.
The architectures of the plurality of neural networks are commonly, and preferably, established by a same human who does the programming of the neural network. One human thus acts as both neural network architect and neural network programmer.
The selecting step preferably consists of choosing one of the several neural networks by a genetic algorithm, the genetic algorithm acting to select that one of the plurality of neural networks that performs best on the training data set. The training of each of the plurality of neural architectures thus permits, along with the choosing, not only the selection of a single one of the plurality of neural networks that performs best on the training data set, but the training of this selected one of the plurality of neural networks to optimally relate the input data to the output data.
This computerized drug dosage prediction method of the present invention may be extended and expanded to account for interaction between at least two, a first and a second, drugs taken concurrently by the Particular Patient. In this case the extended method involves (i) performance of the programming, the training, the selecting and the using in respect of a first drug to predict in a first selected trained neural network an optimal dosage of the first drug for the Particular Patient, and (ii) performance of the programming, the training, the selecting and the using in respect of a second drug to predict in a second selected trained neural network an optimal dosage of the second drug for the Particular Patient.
Then a number of drug interaction neural networksxe2x80x94each having an architecture of one or more slabs collectively relating data inputs of the order of patient traits to data outputs in the form of clinically-determined optimal drug dosage for each drugxe2x80x94are trained. Each of the several drug interaction neural networks is so trained with a training data set drawn from a multiplicity of historical medical records of a multiplicity of persons historically administered the two particular drugsxe2x80x94these records relating the input data to the output data.
The training produces a number of trained drug interaction neural networks. That one of several trained drug interaction neural networks that performs best on the training data set is selected to become a xe2x80x9cselected trained drug interaction neural networkxe2x80x9d. This selected trained drug interaction neural network is then used to predict the optimal dosage of both the first and the second drug for the Particular Patient. By this expanded method each drug""s optimal dosage may be predicted in respect of the other drug""s optimal dosage.
Therefore, in another of its aspects the present invention can still be considered to be embodied in a computerized method of predicting an optimal dosage of a particular drug for a Particular Patient, only in this case drug side effects, as well as drug dosage efficacy, are considered.
In this variant method of the present invention a neural net that relates drug dosage to both drug efficacy and to drug side effects is programmed. This neural network is trained in consideration of both (i) efficacy and (ii) side effect measures from usages of the drug at determined dosages on members of a population, producing by the training a trained neural network.
The trained neural network is subsequently usable to predict a drug dosage for an individual patient that (i) delivers adequate measures of efficacy while (ii) minimizing adverse side effects.
In fact, this using preferably involves exercising the trained neural network in respect of various drug dosages to assess the (i) measures of efficacy and (ii) adverse side effects in respect of each of a number of drug dosages, ultimately selecting the drug dosage for the Particular Patient that (i) delivers the adequate measures of efficacy while (ii) minimizing the adverse side effects.
In still yet another of its aspects the present invention can still be considered to be embodied in a computerized method of diet and/or exercise management.
In the computerized method the effect(s) of one or both of dietary items consumed or exercises performed on the measured physiological characteristics of a Particular Patient is predicted. The method consists of first programming a neural net that relates any of selected dietary items consumed and exercises performed on measured physiological characteristics of patients as might be expected to be affected by the selected dietary items and exercises. The neural network is then trained in consideration of historical data on the impact of the dietary items consumed and exercises performed on the physiological characteristics evidenced by members of a population, therein to produce a trained neural network. Finally, the trained neural network is used to predict the change in physiological characteristics to be anticipated for an individual Particular Patient who consumes any of the selected dietary items and/or performs any of the selected exercises.
The neural network may optionally be repeatedly exercised in respect of various selected dietary items consumed and/or selected exercises performed to assess the (i) measures of efficacy and (ii) adverse side effects in respect of each selected dietary item consumed and/or selected exercise performed. These exercises ultimately permit selecting dietary items and/or exercises that will (i) deliver adequate measures of efficacy to the Particular Patient while (ii) maximizing acceptability and suitability to the Particular Patient""s preferences and demonstrated conduct.
These and other aspects and attributes of the present invention will become increasingly clear upon reference to the following drawings and accompanying specification.